Message from Director-General • • • • • • • • • • • • • • • • • • • • • • • • • • 1Organization of the Institute• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 2
Introduction to Our Research • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 4Strategic Research • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 5
Research Projects • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 12

International Cooperation • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 21
Research Collaboration • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 22
Graduate School Program • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 23
Outreach Activities • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 27
Tutorial Courses • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 27
ISM Seminar • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 28
Special Lecture • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 28
Statistical Consultation Service • • • • • • • • • • • • • • • • • • • • • • • • • • 28
Annual Research Report Meeting for 2005 • • • • • • • • • • • 28

Finance and Buildings • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 29
Facilities and Equipment • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 30
Organization • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 31
History • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 35

Cover design
The images of the continents represented by characters symbolize the abstract
nature of statistical mathematics and the keywords placed on them indicate
the institute's goals. The photo of the super computer in the center symbolizes
the importance of computation, while the four belts around the globe express
the close link between the four research units of the institute.

Message from Director-General

Last April, the Institute of Statistical Mathematics restructured its
research organization and adopted a new research structure comprising abasic research section, a strategic research section and a research support
section. In order to embody our basic principles of establishing new
statistical methods through challenge to important social tasks, the newresearch organization adopted a dual structure consisting of basic researchand strategic research sections, which cover our activities both horizontallyand vertically. In principle, all full-time researchers are assigned to the basic
research section, to conduct basic research from a long-range perspective.
The strategic research section forms research task forces by flexibly andintensively combining researchers in the basic research section, visiting
professors and project researchers.
In the basic research section we have created the three departments ofStatistical Modeling, Data Science, and Mathematical Analysis and Statistical
Inference, thus establishing an organization able to work on the development
and application of important statistical methods. Compared to the basic
research section, the strategic research section will tackle the strategicallyimportant tasks of the institute in a flexible and intensive manner. We havecreated a Prediction and Knowledge Discovery Research Center, and a Risk
Analysis Research Center in this section. The Prediction and Knowledge
Discovery Research Center aims to develop methods and applications forprediction and knowledge discovery based on huge data set, in view of apost-IT society. The Risk Analysis Research Center, on the other hand, aims
to develop scientific methods to deal with the increased uncertainty and risks
caused by globalization. This research center, which was newly founded in
the last fiscal year, comprises three research groups: drug and food safety;
environmental risk; and financial risk and insurance. The center has alsoestablished a risk research network connecting many research organizations
involved in risk research in order to establish a Risk Research NOE (Network
of Excellence).
Two years have passed since the Research Organization of Informationand Systems was established and its research activities are gathering
momentum. In the last fiscal year, a Transdisciplinary Research Integration
Center was established within the headquarters of the organization and new
research projects were initiated with the cooperation of four research
institutes attached to the organization. The Institute of Statistical
Mathematics, in particular, has a key leadership role in the Function andInduction Research Project and has also begun research on a new paradigmof scientific inference suitable for the information age.
As stated above, the Institute of Statistical Mathematics aims to serveboth as a research institute of methodology by conducting joint research withresearchers from various disciplines and to act as a keystone for theexchange of research, thus serving as an Inter-University Research
Organization. We believe that the role of the institute has become more
important with the rise of the information and risk society. We therefore urge
your further understanding and support.

Genshiro Kitagawa
Director-General
The Institute of Statistical Mathematics
1Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling conducts research on the modeling of causally, temporally and/or
spatially interrelated complex phenomena, including intelligent information processing systems. It also conducts
research on model-based statistical inference methodologies
Research Groups

Research Details

Spatial and Time Series
Modeling GroupThe Spatial and Time Series Modeling Group works on modeling and inference for the statisticalanalysis of time series, spatial and space-time data, and their applications to prediction and control.

Intelligent Information
Processing GroupThe Intelligent Information Processing Group works on concepts and methods for the extraction,
processing and transformation of information in intelligent systems, motivated by an active interest inpractical problems in engineering and science.

Graph Modeling GroupThe Graph Modeling Group works on analyses of the data generated by systems with a graph structure
and on the modeling required in order to reconstruct the original system.

Organization of the InstituteDepartment of Data Science
The Department of Data Science aims to develop research methods for surveys, multidimensional data analyses,
and computational statistics.
Research Groups

Research Details

Survey Research GroupThe Survey Research Group focuses on research related to statistical data collection and data analyses.

Multidimensional Data
Analysis Group

The Multidimensional Data Analysis Group studies methods for analyzing phenomena grasped on
multidimensional space and ways for collecting multidimensional data.

Computational StatisticsGroup

The Computational Statistics Group studies sophisticated uses of computers in statistical methodology
such as computer-intensive data analyses, computational scientific methods and statistical systems.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, the theory of optimization, and the practice of statistics in science.
Research Groups

Research Details

Mathematical StatisticsGroup

The Mathematical Statistics Group is concerned with aspects of statistical theory and probability theory
that have statistical applications.

Learning and InferenceGroup

The Mathematical Statistics Group develops statistical methodologies that enable researchers to learn
from data sets and to properly extract information through appropriate inference procedures.

Computational Mathematics The Computational Mathematics Group studies computational algorithms together with mathematicalmethodologies used for statistical modeling in the sciences.
Group

2

Strategic ResearchPrediction and Knowledge Discovery Research Center
The Prediction and Knowledge Discovery Research Center studies the statistical modeling and inference
algorithms that can be used to extract useful information from the huge amount of data which complex systems
produce, and thus attempts to solve real-world problems in many different scientific domains, especially genomics,
earth and space sciences.
Research Groups

Research Details

Molecular Evolution
Research Group

The Molecular Evolution Research Group researches the area of molecular phylogenetics, and seeks todevelop statistical methods for inferring evolutionary trees of life using DNA and protein sequences.

Date Assimilation
Research GroupThe Data Assimilation Research Group aims at developing new, advanced data assimilation techniques
to combine different information from dynamical simulation and observation data.

Statistical Seismology
Research GroupThe Statistical Seismology Research Group is concerned with the evaluation of seismicity anomalies, detectionof crustal stress changes, their modeling, and the probability forecasting of large aftershocks and earthquakes.

Organization of the InstituteStatistical Genome Diversity The Statistical Genome Diversity Research Group aims to construct novel methodologies for learningand inference from a variety of data sets in the rapidly growing area of bioinformatics.
Research Group

Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the study of the increased uncertainty
and risk associated with the increasing globalization of society and the economy. The center is also constructing a
network for risk analysis in order to contribute to the creation of a reliable and safe society.
Research Groups

Research Details

Food and Drug Safety
Research GroupThe Food and Drug Safety Research Group aims to develop the statistical framework and methodology
of quantitative risk evaluation for substances ingested by the human body.

Environmental Risk
Research GroupThe Environmental Risk Research Group studies the statistical methodologies related to environmental
risk and environmental monitoring.

Financial Risk andInsurance Research GroupThe Financial Risk and Insurance Research Group explores the use of statistical modeling methods toquantify the risks involved with financial instruments and insurance products.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.
Unit

Operation

Computing Facility Unit

The Computing Facility Unit is in charge of the management of computer facilities and software for research.

Networking Facility Unit

The Networking Facility Unit is in charge of the management of networking infrastructure used forresearch and is responsible for network security.

Education and Library Unit

The Education and Library Unit is in charge of planning statistical education courses to popularize
research results and is responsible for maintaining an extensive library.

Public Outreach Unit

The Public Outreach Unit is in charge of the publication and editing of research results and is
responsible for public relations.

3

Strategic Research Projects
The ISM is divided into 3 categories; Basic Research, Research Support, and Strategic Research. The Basic
Research section provides a framework for fundamental research, accepting original contributions from all staff
members, with an emphasis on developing statistical methods that can address current societal issues. Research
Support acts precisely as its name implies, as a part of the Inter-University Research Organization. Strategic
Research also combines the strengths of Basic Research, Research Support, and other universities and research
institutes, in order to address important societal issues.
The Strategic Research section has established the Prediction and Knowledge Discovery Research Center andthe Risk Analysis Research Center. These two centers are organized as follows:

Prediction and Knowledge Discovery Research Center (PKDRC)
Molecular Evolution Research Group
Data Assimilation Research Group
Statistical Seismology Research Group
Statistical Genome Diversity Research Group

Introduction to Our Research

Risk Analysis Research Center (RARC)
Food and Drug Safety Research Group
Environmental Risk Research Group
Financial Risk and Insurance Research GroupISM Research Projects
The institute also emphasizes the importance of its own and the exploratory research. Every year we invite
researchers from within the institute to propose new research projects. A relativery sizable amount of funding is
allocated to approved projects, and strategic research leading to future development is encouraged. In this financial
year, we have approved the following ten projects. No limitation was placed on theme or category.

Research Theme

Project Leader

Development of array data analysis

Makio Ishiguro

Probability density estimation with semidefinite programming and related topics

Takashi Tsuchiya

Computational statistics of quantum state estimation

Yukito Iba

Recent developements in information geometry

Shiro Ikeda

Random generation of space group symmetry

Yoshiaki Itoh

Reconstructing functional neuronal circuits from optical imaging data

Yoshiyasu Tamura

Developing predictive models for bycatch in tuna fisheries

Mihoko Minami

New developments of indirect questioning techniques

Takahiro Tsuchiya

A comparative study among survey methods

Tadahiko Maeda

The research activities of each group and a profile of each research project within the Strategic Research Center
are as follows.

4Strategic Research Projects
Molecular Evolution Research Group

Toward Integrative Understanding of Biodiversity[Methods for phylogenetic inference from molecular sequences]
In order to understand the biodiversity, evolutionary view is indispensable, and the method for inferring evolutionary trees from DNA
and protein sequence data is becoming an important tool in this field. In this project, we are developing models of molecular evolution andmethods for inferring molecular evolutionary trees. We have released the program package MOLPHY for phylogenetic inference frommolecular sequences. During the process of developing methods for data analyses, it is important to repeatedly apply the methods to real
biological problems in order to develop useful methods.

[Natural history and biodiversity in Madagascar]
Biological problems we are studying include natural history and biodiversity of Madagascar which is an island isolated from continents
during a long geological period, and we are studying tenrecs, lemurs and baobabs which present remarkable diversity in Madagascar.
Baobabs (Adansonia) are trees adapted to arid environment. In Madagascar 7 species are distributed, while Africa and Australia
respectively have only one species. In order to clarify the evolutionary history of baobabs and to understand why baobabs show
remarkable diversity only in Madagascar, we are analyzing chloroplast genomes of Adansonia as well as several nuclear genes.
Masami Hasegawa

Introduction to Our Research
5Data Assimilation Research Group

Data Assimilation: Information Fusion Technique of Simulation and Large-scale Observation Data

[What is data assimilation?]
Obtaining an accurate prediction of the upper ocean thermal structure is one of the important issues in modeling studies of the ocean
circulation and Earth’s environment. A numerical ocean model-based experiment, i.e., simulation study has been conducted for thispurpose. Dependent variables of the numerical ocean model can be considered as stochastic variables due to the uncertainty in the initialand boundary conditions and the imperfection of the model. A natural idea to compensate for such insufficient information only via
simulations is to combine observations with numerical models. Hence, a reasonable way of blending a numerical model and observation is
now becoming a central issue in the earth science community. Data assimilation is a technique for a synthesis of information from a
dynamic (numerical) model and observation data. It is an emerging area in earth sciences, particularly in meteorology and oceanography,
stimulated by recent improvements in computational and modeling capabilities and the increase in the amount of available observations.

[Data assimilation technique is a time series analysis method!]

Introduction to Our Research

In a statistical sense, data assimilation supposes two models: system model and observation model. The system and observation
models correspond to large-scale numerical model-based simulations and large-scale observation datasets, respectively. The data
assimilation can be therefore formulated in the state space model (SSM). The SSM has given a platform in nonstationary time series andcontrol studies for three decades after Kalman. Past studies for data assimilation employed a linear Gaussian SSM and applied Kalmanfilter. In the meanwhile, many phenomena in earth sciences have nonlinearity and come along with non-Gaussian fluctuations. A highdimensionality of the state vector from ten thousands up to afew millions causes the numerical problems in computation forthe data assimilation. Realization of high performance inprediction ability demands the data assimilation method toevaluate how much the numerical simulation model can describe
the real world phenomena. A purpose of our research group is todevelop a new non-linear non-Gaussian data assimilation
method and propose a framework for evaluating the simulation
models in relevance to real data.

[Data assimilation technique can be applicable to any
domains!]
The ISM is being proud of demonstrating an international
and leading activity in the time series analysis for three decades.
These studies promote us to study the nonlinear non-Gaussian
data assimilation technique based on the ensemble-based
nonlinear filtering and smoothing methods: e.g., the ensemble
Kalman filter, the particle filter, and the Monte-Carlo mixture
Kalman filter. These methods are examined with applications togeophysical phenomena such as El Niño, Tsunami, and ring
current in the earth magnetosphere. A concept of data
assimilation is general, and therefore is applicable to any
problems in which a numerical simulation model and observation
data appear. We are now launching the research project withdata assimilation technique applied to several problems in awide variety of fields.
Tomoyuki Higuchi

Figure : Result of identical twin experiment in the Sea of Japan. False
topography is modified and variety of bottom topography “scenario”
decreases as tsunami propagates.

6

Statistical Seismology Research Group

Anomalous Aftershock Activity Relative to that Predicted by the ETAS Model

Introduction to Our Research

Seismic quiescence and activation, as the precursors to large earthquakes, have attracted much attention among seismologists. Of
particular interest is the hypothesis that the stress-changes transferred from a far-field rupture or silent slip can cause seismic changes insome regions. On the other hand, since a sequence of aftershocks is triggered by complex mechanisms under heterogeneous fractal
media, it is difficult to precisely describe the transfer of stresses both within and near to the field. In other words, triggering mechanics
within an aftershock sequence are too complex to allow us to calculate the effect of stress changes in detail. Nevertheless, we can use
statistical empirical laws as a practical method for describing aftershock triggering. That is to say, fitting and extrapolating a suitablestatistical model for normal seismic activity in a situation without exogenous stress changes provides us with an alternative method
through which to see the seismicity changes explicitly. Thus, one of the main objectives of this project is to show the possibility that
diagnostic analysis based on fitting the ETAS model and its space-time extension to regional seismicity can be helpful in detecting small
exogenous stress changes. Indeed, these changes are so slight that one can barely recognize systematic anomalies in the time series ofdisplacement records in the corresponding geodetic records from the GPS network.
In the figure below the left panels show the space-time plot of the aftershocks (M 3) of the 2003 great Tokachi-Oki earthquake of
M8.0 through to January 2005 where the time scale in the left top and bottom panels are the ordinary time and time transformed by thefitted ETAS model, respectively. The data are taken from the rectangular region shown in the right-side panel which shows the associatedCoulomb-Failure-Stress increments (∆CFS) contours for the majority of the Tokachi-Oki aftershocks on the surface of the plate boundary
due to the assumed aseismic slip off the coast of Kushiro, as delineated in the panel. The dark gray contour region indicates negative∆CFS (stress shadow). The portion of space-time in the left bottom panel becomes relatively activated and quiet compared to theseismicity predicted by the ETAS model in the northern and southern half of the aftershock region, respectively, after about 2 months has
elapsed since the mainshock as indicated by the vertical gray line. This appears to be consistent with the ∆CFS pattern shown in the rightside panel.
Yosihiko Ogata

7

Statistical Genome Diversity Research Group

Genome Polymorphism Analysis – Haplotype-block Identification –We are producing a greater amount of genomic data on a wider scale than ever before. One of typical genomic data is SNP (Single
Nucleotide Polymorphism), which has been detected at several millions of sites. We are studying some associations of SNPs with drug
efficacy, adverse effect and disease towards order-made treatments. Standard statistical methods face to difficult situations to extracteffective information from these data sets, which motivates a new paradigm in statistics.

[Association study and haplotype-block]
The haplotype-block consists of strongly-linked SNPs. In association study, it has been gradually recognized that the study based on
haplotype-blocks is better than that based on a direct application of SNPs (Figure 1). Stimulating discussion with Bioinformatics group ofJapanese Foundation for Cancer Research (JFCR) leads us to propose a novel method for identifying haplotype-blocks from the viewpointof statistical genetics and mathematical statistics. The program package is named ADBlock, which will be downloadable in the internet(Figure 2). The advantageous performance is demonstrated from both the real data and synthetic data sets. (Figures 3 & 4). We areplanning a large-scale association study based on haplotype-blocks by the co-operative study with JFCR.
H. Fujisawa and S. Eguchi

Introduction to Our Research

Figure 1: Analysis based on haplotypes(Figure: Genome Center, JFCR).

Figure 3: 11 SNPs in TAP2 gene. Red and green
colored bands are biologically identified haplotypeblocks; the dotted band is still an unidentified region.
(Jeffreys et al., 2000).

8

Figure 2: A part of output from ADBlock software.

Figure 4: Comparison of various methods based on real data in TAP2
gene. ‘True’ corresponds to Figure 3. The hotspot, nonhotspot, andunidentified spot are denoted by X, 0, and *, respectively. ‘MDBlocks’
is given by Anderson and Novembre (2003); ‘Gabriel’ by Gabriel et al.
(2002). (Data: Genome Center, JFCR).

Food and Drug Safety Research Group

Risk-Benefit Evaluation of Food and Drug

[Mission]
Social responsibility of the food and drug safety program in RARC is to design appropriate databases and statisticalmethods for the safety evaluation of food and drug. Our researches are based on a cooperative research project with visiting
researchers and members from the research group on DM and statistic in JPMA as follows:[Significance of the drug safety database from well-controlled clinical trials]
Clinical knowledge from a single clinical trial with less than 400 patients is usually limited to estimate only general drug
efficacy, but practical clinical guidelines can be obtained through the exploratory data analysis of a statistical databaseconcatenating data from several controlled clinical trials to improve not only the drug efficacy but its safety.

[Modeling for incomplete data]

14
12
6

8

10

MMSE

16

18

20

Introduction to Our Research

Expected natural history of Alzheimer decease (AD) can be estimated by a latent growth curve model after constructing
daily clinical database in which the patients visit the hospitals at different time. Thereby hypothetical monthly repeatedmeasurement data include missing data more than 90%, however the significant accelerated factors of AD can be clarified bythis model fitting. The techniques for incomplete data will be generally effective in the field of food and drug safety evaluation.
Hiroe Tsubaki

0.0

0.5

1.0

1.5
Year

2.0

2.5

3.0

The path diagram of the latent growth curve model and the difference of the natural history between AD and Pseudo SD.

9Environmental Risk Research GroupThe Effort of the Statistical Science towards Solution of an Environmental Problem[Mission]
A risk analysis research center / environmental risk research section aims at performing the contribution towards solution of theenvironmental problem which is a modern subject by developing the optimal new statistical methodology for each environmental problem(for example, the problem of dioxin, the problem of global warming, the problem of continuous use of safe water). Moreover, in order torealize this purpose, in cooperation with the community of environmental science, research is carried out including a visiting teacher or a
project researcher.

[Source identification of dioxins]

Introduction to Our Research

The toxicity of a certain dioxin(Fig.1) is stronger than that of the poison gas, sarin, and it is feared that dioxins threaten human health.
The major sources of dioxins are activities of human beings including agricultural chemicals, bleaching and combustion processes.
Dioxins emitted into environment are absorbed into the body of a human being through lungs and a food chain. Recently, theenvironmental contamination caused by dioxins is frequently detected in individual districts. To solve the problem, it is necessary to
identify the sources. However, it is not easy to identify the sources, because there are many unknown sources of dioxins. Accordingly, weare developing statistical models which enable us to make inference on unknown sources. Additionally, to improve the accuracy of theinference, we are trying to complete the data of dioxins cooperating with National Institute of Environmental Studies and manyenvironmental laboratories of local governments.

[Error analysis of Retrieved CO2 column density from the Earth Observation Satellite GOSAT]
There are some errors which are contained to the retrieved CO2 column density from GOSAT(Greenhouse gases Observing
SATellite)(Fig.2). The errors are due to the given condition of the temperature and water vapor amount, the noises which are caused by
sensor device, the process from the signal (interferogram) which is observed to spectrum, and so on. The purpose of this study is toobtain the confidence interval of the retrieved CO2 column density due to these errors. The error models are constructed until now. Fig.3 isindicated the flow from the spectrum which is inputted to sensor to the observed spectrum, and the error factors which are taken into
account in this study.
Koji Kanefuji

polychlorinated dibenzo-para-dioxins

polychlorinated dibenzofuransdioxin-like polychlorinated biphenyls

Fig.1 Dioxins

Fig.2 GOSAT image (photo: JAXA)

Fig.3 The flow from the spectrum which is inputted to sensor to the observed
spectrum, and the error factors (bold letters). *This figure makes underdiscussion with GOSAT Research Team, National Institute for Environmental
Studies, Center for Global Environmental Research.

10

Fig.4 Water analysis in Mt. Tanigawa(Courtesy by Prof. Nagafuchi)

Financial Risk and Insurance Research Group

Toward Quantitative Analysis of Financial Risk

[Aim of our group]
Financial Risk and Insurance Research Group mainly concerns the theory and practice in the quantitative analysis of risks involved
with various financial instruments and insurance products from the view point of statistical modeling. Various research projects are under
way not only by ISM researchers but by visiting and post doctoral researchers. This article introduces some of many instances.

[Modeling market risks]
Quantitative analysis of market risks is usually done by modeling the dynamics of the return time series or the change of some assets.
Several projects are in progress on jump diffusion models that dispense with complete markets hypothesis assumed in mathematicalfinance. Also, it is well known that the distribution of returns of financial assets often looks fat-tailed. That naturally leads to non-Gaussian
time series analysis where the time series analysis group of ISM has its long history and hence its strength. Displayed figures show option
pricing errors of Black-Scholes model compared to the pricing model using Normal Inverse Gaussian (NIG) distribution.

[Credit risks, securitization]

Introduction to Our Research

A new international regulation on the capital of banks (so-called Basel II framework) will be in effect at the end of 2006. Accordingly,
statistical models to capture credit risks have attracted good deal of public attention. In this connection, project on modeling correlation ofdefault and loss given default, and on the quantitative analysis of debt collection are done. Some are engaged in the measurement of risks
emanating from ‘structured’ products in which derivatives are embedded, and others in the analysis of prepayment risks in mortgagebacked securities. Pushing these works forward, the existing framework of inter-university research cooperation is restrictive, andcooperation with financial industry and regulator is indispensable. Some ISM researchers are appointed as visiting researchers at Financial
Services Agency, while ISM invites researchers from banks and financial institutions as visiting researchers.
Yoshinori Kawasaki

Left panel shows option pricing by Black-Scholes model which assumes Gaussian distribution. Right panel exhibits the payoff surface assuming NIG
distribution. Apparently there seems to be no difference between them.

Taking a close look at the difference of two payoff surfaces reveals the gap. NIG distribution has a much better empirical goodness of fit for return series
than Gaussian distribution. In light of this, option pricing by Black-Scholes model becomes inaccurate as the maturity approaches expiration, especiallyaround ‘at-the-money’. (Graphs provided by Dr. Kenichi Kawai (Research Associate, Transdisciplinary Research Center, Research Organization ofInformation and Systems))

11Research Projects
Research Projects

Development of Array Data Analysis Method

Introduction to Our Research

Analysis of time series data obtained from observation device network laid in a space is a common important topic in various fields.
For example, VLBI (Vary Long Baseline Interferometer) used in radio astronomy is compounded of parabola antennas placed distantly
apart from each other, and catch the feeble radio wave arrived from space simultaneously. We can measure the incident direction of wave
or relative positions of antennas precisely from a little time lag of signals between antennas. On the other hand, in seismology, we observe
seismic wave using the seismometer observation network that arranged on the ground surface (seismic array), and it possible todetermine the position of hypocenter, magnitude, and mechanism of earthquakes. In addition, the measurement of micro-earthquake andmicrotremor (stationary oscillation of the ground when natural earthquakes does not occur) is used in order to estimate undergroundstructure of the place that developed array.
Array observation data of microtremor are used effectively for the purpose of investigation of earthquake engineering and protection
against earthquake disasters.
This method estimates array basement structure of under the layer as horizontal layer structure using nature of dispersion of phasevelocity (phase velocity varies with a wavelength) of the surface wave contained in microtremor .In real observation, we arrange about 10
or 20 seismometers with triangular or circular formation in range from several dozen meter to several km, the radius of array is according
to depth of the basement structure that we wants to estimate, and get time series data for several hours or several minutes. We analyze
these data and estimate phase velocity dispersion of a surface wave which pass the array by frequency-wavenumber method (F - K
method) or space autocorrelation method (SPAC method), and estimate the basement structure (parameters such as the modules ofelasticity or layer thickness) by calculating a reverse problem.
We execute numerical simulations to find an effective method of analysis of these data and make a computer program based on
those results. For example, we set parameters such as basement structure, direction of wave propagation, power spectrum, and noise
structure, and make simulation waves, and analyze it with the method mentioned above again to estimate the parameter more precisely.
Furthermore, based on these results, we are aiming to develop of general computer programs for array data analysis to analyze timeseries data of other fields (radio astronomy or cerebral nerve science, etc.).
Ken Ishikawa

An example of an arrangement of seismic array (10 observation point) (left), and an example of microtremor (right) time series for 20 seconds.

Examples of frequency-wavenumber spectrums calculated from above array data of microtremor .

12

Research Projects

Probability Density Estimation with Semidefinite Programming and Related Topics

Introduction to Our ResearchDensity estimation has been one of the fundamental problems in statistical science and informatics. So far, histogram andfininte mixture normal model are two typical major approaches. In this research, we develop a new method based on recent
development of optimization. We express a density as “the product of nonnegativity polynomial and normal distribution”
and/or “the product of nonnegative polynomial over positive axis and exponential distribution.” We estimate parameters by the
maximum likelihood method and choose the best model by AIC. The main difficulty of this method is estimation of parameters.
This dificulty is overcome with the new optimizaion technology called semidefinite programming. The maximum likelihood
estimation is formulated as maximization of logarithmic determinant problem of a positive semidefinite matrix under linear
matrix inequality constraints. With the techniques of semidefinite programming, we can show that this problem is solved
efficiently.
An ultimate goal of this research is to develop a software which automatically estimates densities from data. Toward thisgoal, we study suitable data structure and numerical techniques for efficient and practical implementation. Another important
issue is dealing with various types of data. Recently, we extended the standard polynomial-time primal-dual interior-point
algorithms for semidefinite programming to the problem of maximizing weighted sum of several logarithmic determinantfunctions. This algorithm enables us estimation of density from histogram data. The developed algorithm also has otherpotential applications in various fields of informatics.
Takashi Tsuchiya

Figure: Estimated density for positive data (simulation)

13

Research Projects

Computational Statistics of Quantum State Estimation[Quantum State Estimation]
It was at the end of the 20 century when we first “see” fundamental quantum objects such as wave functions or Wigner functions,reconstructed from experimental data. The technique required for such visualization is called “quantum state estimation” and considered
as a basis of any quantum technology, say, quantum computation and quantum cryptography.

[Quantum Tomography]
A typical example of quantum state estimation is given by the “optical homodyne detection” shown in the figure. Here the laser beam
X is the one to be measured, and Y is a reference beam whose phase S is controlled by the device B. X and Y enter the beam splitter A
and are mixed. We reconstruct the quantum state of the beam X from the data of the difference of counts at C and C’ measured with a setof phase S. The key of the method is how to solve an inverse problem from the results of the measurement to the quantum state. In thiscase, the inverse problem can be formulated as a Radon transform. Surprisingly enough, this method is the same as the one in Computer
Tomography used in hospitals, and the reconstruction method is called “quantum tomography”.

[Computational Statistics of Quantum State Estimation]

Introduction to Our Research

As seen in the above example, the core of quantum state estimation techniques is how to solve improper inverse problems with noisy
data. It is a problem of central interest in modern statistical science and also has been well studied by researchers at the Institute of
Statistical Mathematics. The computation required for quantum estimation is often reduced to a convex optimization problem, which isalso one of the major research topics at the Institute of Statistical Mathematics. We are now studying this new field of statistical science inthe collaboration with Prof. Matsumoto at the National Institute of Informatics.
Yukito Iba

14

Research Projects

Recent Developements in Information Geometry[Background]
The idea of information geometry emerged in 1980's and is attracting a lot of interests. Initially, it was applied to basic problems ofstatistics and has built a new mathematical framework of them based on differential geometry. Recently, it is applied to wider fields, suchas machine learning, error correction codes, and quantum information theory.

[Aim of the project]
In early years, Japanese researchers including Amari (RIKEN), Nagaoka (Univ. of Electro-Communications), and Eguchi (ISM)
contributed to the fundamental developments of information geometry. The committee including them organized the 2nd international
symposium of information geometry (IGAIA2) with the support of University of Tokyo, RIKEN BSI, the Institute of Statistical Mathematics,
and Waseda University in December 2005 at Univ. of Tokyo. In this project, those who participated in IGAIA2 discussed our results, invitedforeign visitors, and investigated further possible developments of information geometry and its applications.

[Result of the project]

Introduction to Our Research

The members of the project presented their results at the symposium. They are summarized below. We first summarize the results ofstatistics. Eguchi discussed the extension of statistical models based on tubular modeling. Kuriki showed the validity of theapproximation of the random matrix eigen values' distribution with integral geometrical method (Euler characteristic heuristics). Fushiki
showed the analysis of the difference between bootstrap prediction and Bayes prediction. Fujisawa discussed the relation between
outliers and bias from information geometrical viewpoint and proposed a new robust estimation method. Fukumizu constructed infinite
dimensional exponential family with the use of reproducing kernel Hilbert space, which is widely used in machine learning, and discussed
the estimation problem. For the machine learning, we have two results. Eguchi investigated AdaBoost methods based on informationgeometry, and his co-authors presented recent results. Ikeda has built the information geometrical framework to analyze the belief
propagation algorithm which is widely used in various fields such as error correcting codes, artificial intelligence, and statistical physics.
These results would be applied to improve the performance of the practical engineering systems in the future. We also organized invitedlectures at ISM. We invited G. Pistone (Politecnico of Turin), M. Henmi (Univ. of Warwick), and J. Zhang (Univ. of Michigan). We willcontinue collaborations with M. Henmi and J. Zhang for further developments of information geometry.
Shiro Ikeda

Fig. 1. Robust estimation and information geometry of proposed method

Fig. 2. Graphical model and information geometry of belief propagation15

Research Projects

Statistics on Discrete Geometry

What is the most probable structure in nature?
We introduced a method that defines the species (representatives) of inorganic compounds, and studied the statistical
distribution of the defined species among space groups, by using ICSD (Inorganic Crystal Structure Database ). We found thatthe number of formula units in a unit

cell gives a natural classification to understand the statistical distribution of symmetrygroups. To analyze the distribution, the network of the subgroups and super groups among space groups will be helpful.
We studied discrete random sequential packings. The space group O 7 is generated by a discrete random sequential
packing of cubes into torus. The random sequential packing of caps into a sphere helps to understand the geometricalstructure of diatoms or pollen.
This project is international with members, Michel Deza (Ecole Normale Superieure, European Academy of Sciences),
Nikolai Dolbilin (Steklov Mathematical Institute, Moscow State University), Mathieu Dutour (Rudjer Boscoviz Institute), Hiroshi
Maehara (Ryukyu University), Yoshiaki Itoh, Teruhisa Sugimoto, Masaharu Tanemura (The Institute of Statistical Mathematics) .
Yoshiaki ItohIntroduction to Our Research16Research Projects

Reconstructing Functional Neuronal Circuits from Optical Imaging Data

[Introduction]
There are 14 billion neurons in a human brain. When we think or move our body, these neurons are at work. Even when we breathe
un-consciously, neurons give a command to breathe. Neurons do their jobs by constituting functional neuronal circuits and mutually
exchanging information. Our project is to elucidate the flow of information within the circuits by statistical mathematics.

[Knowing the brain top-down method and bot-tom-up method]
There are two methods to study how the brain works. The first is the top-down method, where we understand the brain by
information-processing models that simulate the action of the actual brain. The second, called as the bottom-up method, is to understandthe brain by making up the neuronal circuitry from various parts such as neurons, channels, and genomes. Too many parts have been
hindering the progress of the bottom-up method, and the top-down method has gained the popularity.

[Understanding the brain by the bottom-up method]

Introduction to Our Research

If we want to understand the brain by the bottom-up method, we need to know the diagram of neuronal circuits, showing which
neurons are connected to which neurons. To accomplish this, we must record neuronal activities from multiple neurons simultaneously.
One of the methods is the optical recording, where changes in absorption or fluorescence corresponding to the changes in membranepotentials are recorded. This method has been applied to the central nervous system since 1980’s. A rapid progress in photo-electronics
improved both the temporal and the spatial resolution significantly, and now we can challenge to unveil neuronal circuits.

[Exploiting the future of neu-roscience with optical imag-ing and statistical mathemat-ics]
Multiple autoregressive analysis predicts the future behavior from the past that is recorded as multi-channel time-series data. It canestimate the existence of feedback loops and the causality between different channels. Using this technique, we are analyzing which
neurons send command signals to which neurons, for example, when the animal breathes. Our goal is to establish the analytical procedure to elucidate information process within neuronal circuits from optical imaging data.
Yoshitaka Oku

Figure 1: (Left) Optical image of the neuronal activity that commands
breathing in a newborn rat. (Top-Right) Respiratory neuronal activity
recorded from the spinal cord. (Bottom-Right) Time-series data of a pixelon the optical image.

Figure 2: (Left) Respiratory-related brain regions
estimated by the cross-correlation method.
(Right) Respiratory-related brain regions
estimated by the multivariate autoregressive
analysis.
(Photographs offered by Hyogo Medical College and Chiba University)

17

Research Projects

Developing Predictive Models for Bycatch in Tuna Fisheries

Historically, when people have considered the impact of fisheries on marine resources, their concerns have largely focused on the
population status of the target species. In the last decade, however, the issue of bycatch reduction has been getting more attention.

[Bycatch problem in tuna fisheries]
In the eastern Pacific Ocean, purse-seine nets are used to catch tunas. In this fishery, tunas are detected in three ways: in association
with floating objects ("floating object" sets), in association with herds of dolphins ("dolphin" sets), and as free-swimming schools visible atthe surface. Incidental mortality of dolphins, sharks, sea turtles, and other species can occur during fishing operations for tunas. The
incidental mortality of dolphins in this fishery was the first bycatch problem that attracted public attention. In 60's, hundreds of thousandsof dolphins are estimated to have been killed annually incidental to fishing operations in dolphin sets. With the development of dolphinrelease techniques by fishermen, national legislation and international agreements establishing quotas on incidental mortalities, and theimplementation of a seminar program designed to educate fishermen on methods for avoiding dolphin mortalities, incidental mortality ofdolphins has declined to less than three thousand animals annually since 1998.

[Prediction of shark bycatch]

Introduction to Our Research

Although dolphins are rarely killed incidental to fishing operations in floating object sets, large amounts of bycatch of many otherspecies can occur in these sets. We are working with Dr. Cleridy Lennert-Cody of the Inter-American Tropical Tuna Commission to
analyze shark bycatch data in floating object sets. Annually, shark bycatch occurs in more than one third of the floating object sets. We
are exploring new statistical techniques for the prediction of the occurrence of shark bycatch, including boosting methods, which arerecently proposed discrimination methods. Our preliminary results suggest that boosting techniques give more stable predictions thanexisting discrimination methods for these data.

[Analysis of shark bycatch counts]
One notable characteristic of shark bycatch data is that there are many sets with zero bycatch, yet sets with large amounts of bycatch
can also occur. Thus, we are developing a zero-inflated negative binomial regression model for the shark bycatch data. The zero-inflated
negative binomial regression model assumes that there are two states: a "complete" state in which bycatch never happens, and an"incomplete" state in which bycatch might occur. In the incomplete state, bycatch counts are assumed to follow a negative binomial
regression model. We have fit this model to shark bycatch data from floating object sets. The distribution of the predicted bycatch was
found to be quite similar to that of the observed bycatch suggesting that the zero-inflated negative binomial is a reasonable model forthese data. We will use this model to explore trends in shark bycatch rates.
Mihoko Minami
1993-2003

Floating object sets

Dolphin sets
Figure 1. Locations of purse-seine sets

Figure 2. Observed and predicted distributions for shark bycatch counts

18

Unassociated sets

Research Projects

New Developments of Indirect Questioning Techniques[Indirect questioning techniques]
In social survey research, direct questioning technique, in which respondents are requested to answer questions directly, is usuallyused. However, it is not expected that respondents would give truthful and credible answers when the questions are about private mattersor socially undesirable issues. Hence, some indirect questioning techniques, which aim to conceal each respondent's true status and to
estimate the proportion of people who have engaged in a stigmatizing behavior, have been developed. Indirect questioning techniques are
expected to elicit more truthful answers than the ordinary direct questioning technique because no one could know the respondent’s true
status. The need for the indirect questioning techniques is supposed to increase in the near future because Japanese people are recently
very sensitive about their private information.

[New developments of IQT]
The randomized response technique is one of the well-known indirect questioning techniques. Though the technique is theoreticallyinteresting, it is not a practical one because its procedure is difficult for respondents to understand and the technique is impossible to be
implemented except in the interviewer-administered surveys. Recently, the new IC, UC and TC techniques have been developed. Their
practical realization is expected as alternatives of the randomized response technique.

Introduction to Our Research[Achievement of research project]
In this research project, we aim to realize new indirect questioning techniques via simulation studies and comparative experimental
surveys. First, we succeeded in developing new estimators which have smaller standard errors than traditional estimators. Furthermore, it
is observed that the indirect questioning technique occasionally underestimates the true percentages. The cooperative research withpsychologists has revealed that the underestimation is partly attributable to a psychological response process, which we named “pooling
effect.” We aim to improve the precision of estimators by modeling this response process.
Takahiro Tsuchiya

19

Research Projects

Research on Information Integration in Socio-economic Systems– A Comparative Study among Survey Methods –[The background of research]Society is a system with a very complex hierarchical structure. It seems to be a common situation in all the domains of modern
society where the information acquired through a conventional system is imperfect and the quality is deteriorating, or integrative use of
information has not been achieved. Survey research is an important means for capturing social and economic phenomenon correctly, but
the situation is the same for survey methodology in that structural change of society demands the reformation of the traditional methods.

[Research on new survey methods]
Survey administrated by face to face interviewing based on a random sample, which is a standard survey procedure, has greeted thecrisis in recent years because of the decline in response rate and deterioration of the data quality. On the other hand, new survey methods
such as computer-assisted telephone interviewing and web based internet survey, which are more adapted to the information society, hascome to be widely used. Establishment of the methodology for integrating the information collected through various new methods andmaking effective use of such information is now awaited.

[Purpose of this project]

Introduction to Our Research

Though this research project can be placed in the broader context described above, the study specifically focus on the survey modecomparison. To be concrete, the purpose of this project is to compare the two survey mode, one is the traditional mail survey, and theother is internet survey where pre-registered monitor respondents answer the questionnaire placed on a web site. For the effective use of
results obtained in different survey methods, it is important to know the characteristics of both methods precisely.

[An example of research results]
These three figures illustrate the differences between internet survey and traditional mail survey. In each figure, proportion of answers
to the response options of the same questionnaire items in two different survey mode or two subgroups in the same survey mode areplotted as a scatter diagram. If the plots are on the diagonal line, or to be precise, if the plots are scattered within the statistical error
shown by the dotted line, we can see that the two results are consistent.
In reality, however, results of the mail survey and the internet survey do not coincide very well (Figure1). Part of the reason is that there
are both internet users and non-users in the respondents of the mail survey and response tendency among of the latter is different fromthe former (Figure2). But even if we restrict our attention to internet users in the mail survey, difference larger than the statistical error still
remains between the mail and internet surveys (Figure 3). The reason for the difference should further be investigated.
Tadahiko Maeda

Fig.1. Difference in response percentage
between mail survey (horizontal axis) andinternet survey (vertical axis).

20

Fig.2. Difference in response percentages
between non-internet users (horizontalaxis) and internet users (vertical axis) inmail survey.

Fig.3. Difference in response percentage
between the net users in mail survey(horizontal axis) and internet survey(vertical axis).

Associated Foreign Research Institutes
The Statistical Research Division of the U.S. Bureau of the Census

USA (Washington)

July 27, 1988-

The Stichting Mathematisch Centrum

The Kingdom of the Netherlands(Amsterdam)

May 10, 1989-

Statistical Research Associates Ltd.

New Zealand (Wellington)

October 2001
- October 2006

Statistical Research Center for Complex Systems,
Seoul National University

The Republic of Korea (Seoul)

October 17, 2002-

Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany (Berlin)

December 8, 2004-

Institute of Statistical Science, Academia Sinica

Taiwan (Taipei)

June 30, 2005-

The Steklov Mathematical InstituteRussia (Moscow)

August 9, 2005-

Central South University

China (Changsha)

November 18, 2005-

Soongsil University

The Republic of Korea (Seoul)

April 27, 2006-

Academic Exchange Agreement with the Central South University of China
The Institute of Statistical Mathematics has entered intoan Academic Exchange Agreement with the Central SouthInternational Cooperation

University of China. This is the eighth such agreement,
following agreements with the US Census Bureau, theInstitute of Science of Academia Sinica of the Republic ofChina, the Institute of Statistical and Econometrics of
Humboldt University, and the Institute of Mathematics of theRussian Science Academy, among others. Both parties haveagreed to promote active academic exchanges in the future.
The signing of the agreement took place at the Central

Signing

South University in Changsha city, China, on November
18th, 2005.

21

Research Collaboration
This academic study program provides researchers from other academic institutes with access to the facilities of
the Institute, and provides opportunities for researchers to conduct theoretical and applied studies on statistics.

Fields of Research Collaboration
The research collaboration service is categorized into the following fields. Applicants can use the table below tofind the most appropriate type of project.
Fields

Theory andMethodology

Research Collaboration

Application

Major research domains
1. Fundamental theories

Statistical inference theory, applied probability theory, fundamental studies of data
analysis, history of statistics, statistics education

2. Computation andoptimization

Optimization, large linear computation, super high dimensional numerical integration,
importance sampling, random number, neural netwprls, time series, inferencecomputation

3. Time series

Modeling, analysis, prediction and control of time series

4. Survey theories

Acquisition and analysis method of statistical data, studies on the theory and method
for statistical proof

5. Science and engineering

Practical use of statistics for science engineering

6. Space and earth science

Statistical data analysis related to earth, planetary and space sciences

7. Biology and medicine

Statistical studies in biology, medecine, pharmaceutics, agriculture, life science, etc.

8. Humanities andsocial science

Studies of statistical data in humanities or social science

9. Environment science

Statistical studies on the environment and ecology

10. Others

Other research fields

Fields

Theory andMethodology

Application

2001

2002

2003

2004

2005

1. Fundamental theories

21

22

18

18

18

2. Computation andoptimization

9

8

7

12

13

3. Time series

20

16

13

15

14

4. Survey theories

4

2

3

2

3

5. Science and engineering

11

10

6

10

11

6. Space and earth science

4

8

5

7

11

7. Biology and medicine

23

26

25

24

26

8. Humanities andsocial science

15

13

10

9

12

9. Environment science

9

6

10

9

11

10. Others

4

3

2

2

5

120

114

99

108

124

Total

22

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced Studies(the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its
opening, the Institute has created the Department of Statistical Science and, since April 1989, has accepted students
for education and research in doctoral programs. In 2006, the Institute adopted a five-year system, offering either afive-year education and research program, or a three-year education and research program starting from the thirdyear of study.

Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science

Graduate School Program

Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science
Accelerator Science

President

School of High Energy
Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Biosystems Science
Photo Science

Outline of Education and Research
The course includes modeling, forecasting, inference and designing of data-gathering systems in order to extract
information and knowledge from the real world based on empirical data, as well as basic, mathematical and applied
education and research related to these fields. The course aims to provide the student with skills that help tocontribute to solving important and connected issues and give the ability to perform original research.

23

Field of Education
and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as Spatial and space-time Modeling, GraphicalModeling of temporally and/or spatially inter-related complex phenomena, and Intelligent Information
Processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statisticallearning theory to extract information from data by automated learning and inference techniques; as well as
theory and applications of optimization and computation algorithms which serve as the basis for computational
inference.

Features of Education and Research•

The course is the only integrated doctoral program on statistical science in Japan. It has received students from awide variety of disciplines and has offered education and research on statistical science by professors specialized
in many different fields, from theory through to practical applications.

Graduate School Program•

The Institute of Statistical mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well as avariety of software, including original statistical software developed by the institute.

•

The academic publications and books on statistical and mathematical sciences produced are some of the best in the world.

•

The library holds an extensive collection of books and journals, being one of the best in the world.

•

In its role as an inter-university research institute, the Institute holds frequent workshops and seminars by visiting
professors and researchers from both Japan and abroad. Students are free to attend and participate.

•

It is possible to collaborate with researchers from other universities and institutions. It is also possible for students
to develop their own projects by participating in research projects with other institutions through theTransdisciplinary Research Integration Center, Research Oraganization of Infomation and Systems.

Course Requirements and Type of Degree Granted•

Requirements to complete the course are as follows:
Completion of at least 40 credits while a graduate student of five years, or completion of at least 10 credits while a
doctorate student of three years who graduated Master’s course meeting all the criteria set by the thesis
committee of the Institute and successfully completing the final examination.

•

On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with an interdisciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

•

24

The required number of years of study will be flexible if a student demonstrates outstanding research results.

Enrollment Quota and Number of Students
Doctor's course five years

Doctor's course three years
Year of enrollment

2001

2002

2003

2004

2005

2006

2006

Quota

4

4

4

4

4

3

2

1

2

6

7 (1) 6

2

1

Actual number of students

The figures in parentheses indicate the number of foreign students being supported at government expense.
The figures in circles indicate those who are employed by other organizations.

University Background of Students

 Private universities
Okayama University of Science
Tokyo University of Science (4)Kyoto Sangyo University
Keio University (2)Waseda University (5)Chuo University (4)Toyo University
Nihon University (2)Hosei University (7)Graduate School Program National and public universities
Ochanomizu University
Tohoku University (2)Hokkaido University
Hitotsubashi University (4)Kyushu University
Chiba University
Osaka University (2)Osaka City University
University of Tsukuba (2)Shimane University (2)The University of Tokyo (12)Tokyo Gakugei University
Tokyo Institute of Technology (2)Tokyo University of Marine Science and Technology
Tokyo University of Agriculture and Technology
Fukushima University
Japan Advanced Institute of Science and Technology, Hokuriku
Nagoya University (2)Kyoto University (2)Saitama University

 Foreign universities
Universidade Estadual of Campinas
University of Colorado at Boulder (2)University of Dhaka (2)University of Hawaii
Jahangirnagar University (3)University of Malaya
Ohio University
University of Rahshahi
Stanford University
Center for Analysis and Prediction, China Seismological Bureau
Northeastern University, China
The Hong Kong University of Science and Technology
University of Science and Technology of China
Chinese Academy of Sciences, Institute of Applied Mathematics

Degrees AwardedYear

Doctor of Philosophy

Year

Doctor of Philosophy

1993
1994
1995
1996
1997
1998
1999
2000

2
7
6 [1]
3 [1]
1
4 [1]
6
5

2001
2002
2003
2004
2005

5
4
8 [3]
4
4

[ ] Ph.D. on the basis of the dissertation only (included in the total)

25

Current Position of Alumni

Graduate School Program National and public universities, and public organizations
University of Tsukuba Professor
Saitama University Associate Professor
The University of Tokyo Associate Professor
The University of Tokyo Special Duty Research Fellow
The University of Electro-Communications Associate Professor
University of Hyogo Associate Professor
The Institute of Statistical Mathematics Associate Professor
Kyushu Institute of Technology Associate Professor
University of Tsukuba (2)Hokkaido University AssistantChiba University Assistant
The University of Tokyo AssistantTokyo Institute of Technology AssistantTokyo University of Marine Science and Technology Assistant
Hiroshima University Assistant
Kyushu University Assistant
University of the Ryukyus Assistant
The Institute of Statistical Mathematics Assistant
The Institute of Statistical Mathematics Research Fellow (2)Railway Technical Research Institute Research Fellow
Nara Institute of Science and Technology Research Fellow
Tokyo Institute of Technology Research Fellow
Riken Research Fellow
Research and Training Institute of the Ministry of Justice Researcher
Railway Technical Research Institute Research Fellow
Bank of Japan
 Private universities
Asahikawa University Professor
Sapporo Gakuin University Professor
Doshisha University Professor
Meiji University Professor
Tokyo Health Care University Associate Professor
Josai University
Tokyo University of Information Sciences
Sapporo Gakuin University (2)Aoyama Gakuin University AssistantTokyo Women's Medical University Research Fellow
 Foreign universities
Jahangirnagar University (2)Victoria University
Hong Kong Baptist University
Massey University Research Fellow
University of Otago Research Fellow
University of Rahshahi Associate Professor
University of California, Los Angeles Research Fellow
Asia-Pacific Center for Security Studies Associate Professor
University of South Carolina Research Fellow
The University of Warwick Research Fellow

26

 Others
Hitachi, Ltd. Central Research Laboratory Research Fellow
NTT Communication Science Laboratories Research Fellow
NLI Research Institute Research Fellow
Mizuho Trust and Banking Research Fellow
Shinshowa Corporation
Sankyo Co.,LTD.
ATR Computational Neuroscience Laboratories Research Fellow
Mitsubishi UFJ Wealth Management, Ltd.
Toyota Motor Corporation, Higashi-Fuji Technical Center Research Fellow
Schlumberger Limited
Statistical Information Institute For Consulting and Analysis

Tutorial Courses
History
In 1944, the year that the Institute of Statistical Mathematics was founded, an education program for employedpeople was initiated at the 1st Period Numeric Computation Training Center of the Science Research Technical
Assistant Training Center of the Ministry of Education. In 1947, the affiliated Statistical Technician Training Center was
opened as an educational organization for statistical technicians and instructors, in order to improve the levels of staff
training within the statistical organizations of the government and to supply extra statisticians.
As social needs have changed, the purpose of the education program has gradually shifted away from the initial
aim of supplying well qualified statistical technicians for the government, towards statistical education for employedpeople. Tutorial courses were therefore initiated. Later statistical methods became more popular and were applied to awider range of fields. Consequently, more extensive and sophisticated statistical education was required. To meet thisdemand, tutorial courses were expanded to cover a wider range of statistical topics. From 1965 to 1985, six to eight
courses were held annually, not only in Tokyo, but also in provincial cities such as Osaka, Okayama and Fukuoka.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation, and theaffiliated Statistical Technician Training Center was abolished. However, in response to consistent public demand for

Message
Outreachfrom Director-General
Activities

tutorial courses, the Center for Engineering and Technical Support, together with other departments, has run three tofour courses annually. In 2005, the number of courses had risen to 13.

Courses
The total number of courses held from 1969 to 2005 was 205, with a total of 16,606 participants. These courses
covered a wide range of fields from basic to applied statistics. The following table lists the courses held in the past 5 years:
Year
2001

2002

2003

2004

2005

Category

Title

Month

Number ofparticipants

Basic course

Introduction to Statistics

July

93

Standard course A

Statistical Analysis for Resource Management

October - November

16Standard course B

Introductory Course in Quantification Methods

November

49Standard course C

The Latest Theories and Applications on Information
Telecommunications concerning Statistical Mathematics

December

15
95

Basic course

Introduction to Statistics

July

Standard course A

Introduction to Monte Carlo Filter and its Application

October

71

Standard course B

Frontiers in Statistics, Neural-net, Learning - Part 2

November64

Advanced course

How Can New Technologies of Computer Science
Promote Statistical Science ?

March

38
91

Basic course

Introduction to Statistics

July

Standard course A

Introduction to Sampling Methods and Sample Surveys

July

93

Standard course B

Textual Data Mining and its Applications

November72

Advanced course

Markov Chain Monte Carlo—Algorithms and Applications to Statistical Science—

February

82

Basic course

Elementary Statistics

October

93Standard course

Recent Topics from Machine Learning

November91

Advanced course

Speech Processing —For Development of New Statistical Methods—

December

59
85

Basic course

Introduction to Statistics

September

Advanced course

A Junction of Informatics —Chordal Graph and its Applications—

September

13Standard course

Analysis of Qualitative Data by Quantification Methods

October50

Basic course

Nonlinear Time Series Analysis of Financial Data

October52

Basic course
Advanced course

Introductory Data Analysis with R

November

Data Processing and LSI Design for Information andTelecommunications with the Latest Technologies

November - December

98
5

Basic course

Introduction to Sampling Methods and Sampling Surveys

December75

Basic course

Introduction to Reliability Theory and Survival Data Analysis with R

December

39Standard course

Theory and Practice Inferring Molecular Phylogenies

January

72

Basic course

Introduction to Probabilistic Evaluation of Risk

January

45

27

Year

Category

2005

Title

Month

Number ofparticipants

39Standard course

Non-Poisson Regression Models for Count Data

February

Advanced course

Packing and Random Packing

February

7

Basic course

Introduction to Time Series Analysis

March

72

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/

ISM Seminar
The Institute holds a 1-hour seminar every Wednesday starting at 1:30 PM, in the seminar room on the second
floor of the annex building. Teachers of the Institute and other Japanese or foreign researchers will be invited to
speak. This seminar may be held in association with other remote universities or academic organizations via a spacecollaboration system (SCS). On that occasion, it will be held in the lecture hall on the second floor of the mainbuilding. The seminar is open to everyone. For details of the seminar schedule and further information, please visit
the website of the Institute of Statistical Mathematics. http://www.ism.ac.jp/

Special Lecture
We hold a special lecture during Education and Culture Week every year (November 1 through 7), to introduce

Outreach Activities

an aspect of the Institute's activities and to promote statistical science. Several lecturers will be invited to talk about
particular topics, and they will clearly explain up-to-date topics involving statistical science. The topics of 2003 and2004 were: "Prediction and discovery" and "The history of life and the future of the human race from the perspectiveof genes - From evolution to medical treatment". In 2005 this special lecture was held at the Hitotsubashi Memorial
Hall to mark the opening of the Risk Analysis Research Center. The lecture is open to everyone. For details of theschedule of the special lecture program, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/

Statistical Consultation Service
The Institute provides a consultation service on statistical science for the general public and researchers as ameans of actively sharing the benefits of our research with society. This service operates throughout the year.
Please contact the Center for Engineering and Technical Support. The consultation service covers a variety of topic
ranging from the basics of statistics to more specialized issues. Half of the consultants are from the private sector,
and the rest are staff from public organizations, university teachers, and students. Each teacher at the Institutedirectly takes charge of about 20 specialized cases annually, about 40% of which are publicized at academic
conferences, as being of benefit to society.

Annual Research Report Meeting for 2005
The last Annual Research Report Meeting of the Institute was held on March 16 and 17, 2006. This annual
meeting is an opportunity for the Institute's teachers and visiting teachers to present the results of their studies fromthe previous year. The meeting has been held every year since the foundation of the Institute in 1944. In the earlyyears, there were few staff members and participants were therefore able to have enthusiastic discussions all day
long. However, as the number of the staff members has increased, the meeting is now held over two days and eachpresenter is given 10 minutes to report on the results of his/her own research. A wide variety of topics are covered,ranging from statistical contributions to issues facing today's society to more fundamental studies.
This year's meeting started with the opening address by Yoshiyasu Tamura, and the greetings from Genshiro
Kitagawa. A total of 51 research education staff, 9 visiting teachers then gave their presentations. From 2004, wehave begun to compile and distribute proceedings in advance. We also host visitors from other organizations. The
program for this meeting is available from the website. http://www.ism.ac.jp/28

Administration Subsidy and Others

2005

Unit: ¥1,000

Type

Expenditure

Personnel expenses

891,227

Non-personnel expenses

869,621

Total

1,760,848

Accepted External Funds

2005

Unit: ¥1,000

TypeIncomeSubcontracted research

3

5,490

Contribution for scholarship

7

7,050

Total

10

12,540Grant-in-Aid for Scientific Research

MessageFinancefromandDirector-General
Buildings

Items

2005

Unit: ¥1,000

Research Category
Fundamental study (A)Items

Amount Granted

Research Category

Items

39,260Exploratory research

1

1,800

4

Amount Granted

Fundamental study (B)437,800Research by young researcher (B)

10

9,800

Fundamental study (C)1010,800Special researcher award

6

6,700

Total

40

101,160

Site and Buildings· Site Area ──────── 5,033m2· Area for Buildings (total) ─ 6,305m2

As of April 1, 2006

Name of Building

Floors Total Area

OfficeR34,855m2

Information Statistics Research Laboratory

R3

1,024m2

Computation Laboratory

R2

368m2

Gym, etc.

S1

58m2

29

Computation Resources

As of April 1, 2006

Since January 2004, “Supercomputer System for Statistical Science” hasbeen in operation and has analyzed a large volume of statistical data. The main
components of this system comprise a SGI Altix3700 Super Cluster (a parallel
computer system (256 Itanium2 processors and about 2 TB main memory)), a
NEC SX-6 (a vector-type computer system (12 vector processors and 128 GB
main memory)), and a HITACHI SR11000 (a parallel computer sub-system (64Power4 + processors, 128 GB main memory)). In January 2006, “System for
Computational Statistics” was renewed. The main components of this system
consist of a HP XC4000 Cluster system (256 Opteron processors for computing
node and 640 GB main memory), a SGI Prism visualization system (16 Itanium2
processors and 32 GB main memory), and a large display system (Multi Opt View).
In December 1998, an Ethernet network using 1000Base-SX as a main trunk
and 100Base-TX as branches was laid out as a Local Area Network.
Workstations, personal computers in researchers' offices, “Supercomputer
System for Statistical Science”, and “System for Computational Statistics” were

SGI Altix3700 Super Cluster, a parallel computer

Facilities and Equipment

all connected to the network. This Local Area Network enables distributed processing and computation resources
and statistical data to be used effectively. The development of programs, which run on “Supercomputer System for
Statistical Science” and on “System for Computational Statistics” by operating from workstations and personal
computers in researchers' offices, is also underway. To encourage joint research with researchers both in Japan andabroad, as well as the exchange of e-mails, the network is connected to the internet through SINET. The connection
speed of 1.5 Mbps during FY 1999 has risen to 100 Mbps since July 2002. Since October 2003, the network has also
been connected to the super SINET with 2.4 Gbps bandwidth. Some machines are able to communicate at a rate of
1 Gbps. Through terminal servers, the network is also accessible from a public line. In addition, comprehensive
network security measures have been implemented such as the adoption of anti-virus software as well as a network
monitoring system.

Library and Materials

As of April 1, 2006

We have a large number of major Japanese/foreign journals covering a wide variety of fields including statistics,
mathematics, computer science and informatics. In addition, we also have a large library consisting of books onhumanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign publications, we also have a collection of journals that we publish
ourselves: Annals of the Institute of Statistical Mathematics (English; Springer), Proceedings of the Institute of
Statistical Mathematics (Japanese), Research Report (Statistical Researches mainly related to the Japanese National
Character), Computer Science Monographs, Cooperative Research Reports (for collaborative research projects),
Research Memorandum, ISM Reports on
Japanesebooks

Foreignbooks

Japanese
periodicals

14,27927%

Statistical Computing, and ISM Report on

Foreignperiodicals

Research and Education.

44%
Books
52,02273%

56%

meet the needs of researchers working in a

2,172
962

37,74330

All materials are properly catalogued andcan be searched from the web in order toPeriodicals

wide of fields. We also accept photocopy
requests.

1,210

Organization Diagram

Council
Cooperative
Research
Committee
DirectorGeneral

As of April 1, 2006

Spatial and Time Series Modeling GroupIntelligent Information Processing Group
Graph Modeling Group

Department of Data Science

Survey Research GroupMultidimensional Data Analysis Group
Computational Statistics Group

Department of Mathematical
Analysis and Statistical Inference

Mathematical Statistics Group
Learning and Inference Group
Computational Mathematics Group

Prediction and Knowledge Discovery
Research Center (PKDRC)

Statistical Genome Diversity Research Group
Data Assimilation Research Group
Statistical Seismology Research Group
Molecular Evolution Research Group

Risk Amalysis Research Center(RARC)

Food and Drug Safety Research Group
Environmental Risk Research Group
Financial Risk and Insurance Research Group

Center for Engineering andTechnical Support

Computing Facility Unit
Networking Facility Unit
Education and Library Unit
Public Outreach Unit

Vice Director-General
Administration Planningand Coordination Unit

Administration
OfficeGeneralAdministration
SectionGeneral Affairs Unit
Personnel Unit
Research Cooperation Unit

Accounting
SectionGeneral Affairs Unit
Auditing Unit
Supplies Unit
Property Maintenance Unit

Number of Staff
Type
Director-General

As of April 1, 2006

Director-General

Professor

Associate
Professor

Assistant
Professor

Clerk

Technical
Staff

1

Total
1

Department of
Statistical Modeling

6

6

4

16

Department of
Data Science

8

8

5

21

Department ofMathematical Analysis
and Statistical Inference

5

5

3

13

Center for Engineeringand Technical Support

1

Administration Office
Total

Message Organizationfrom Director-General

Department of Statistical Modeling

1

19

19

13

10

11

14

2

16

14

12

78

31

Staff
 Director-General
Genshiro KITAGAWA

As of August 1, 2006

Toshio OHNISHI, Assist. Prof.
Toshihiko KAWAMURA, Assist.Prof.
Computational Statistics Group Vice Director-General

Yoshiyasu TAMURA (Vice Director-General), Prof.

Masaharu TANEMURA

Junji NAKANO, Prof.

Yoshiyasu TAMURA

Yoshinari FUKUI, Visiting Prof.

Tomoyuki HIGUCHI

Makoto TAIJI, Visiting Prof.
Makoto MATSUMOTO, Visiting Prof.

 Department of Statistical Modeling

Naomasa MARUYAMA, Assoc. Prof.

Spatial and Time Series Modeling GroupKoji KANEFUJI, Assoc. Prof.

Tohru OZAKI, Prof.

Seisho SATO, Assoc. Prof.

Masaharu TANEMURA (Vice Director-General), Prof.

Tohru ONODERA, Visiting Assoc. Prof.

Yosihiko OGATA, Prof.

Takeshi KOSHIBA, Visiting Assoc.Prof.

Tomoyuki HIGUCHI (Vice Director-General), Prof.

Nobuo SHIMIZU, Assist. Prof.

Yoshinori KAWASAKI, Assoc. Prof.
Kenichiro SHIMATANI, Assist. Prof.
Genta UENO, Assist. Prof.
Intelligent Information Processing Group Department of Mathematical Analysis
and Statistical Inference
Mathematical Statistics Group

Organization

Makio ISHIGURO (Director), Prof.

Katuomi HIRANO (Director), Prof.

Atsushi FUKASAWA, Visiting Prof.

Satoshi KURIKI, Prof.

Toshio IRINO, Visiting Prof.

Takaaki SHIMURA, Assist. Prof.

Yukito IBA, Assoc. Prof.

Yoichi NISHIYAMA, Assist. Prof.

Yumi TAKIZAWA, Assoc. Prof.

Learning and Inference Group

Tomoko MATSUI, Assoc. Prof.

Shinto EGUCHI, Prof.

Kenji FUKUMIZU, Assoc. Prof.

Kunio SHIMIZU, Visiting Prof.

Hiroshi SOMEYA, Assist. Prof.

Mihoko MINAMI, Assoc. Prof.

Graph Modeling Group
Masami HASEGAWA, Prof.

Shiro IKEDA, Assoc. Prof.
Hironori FUJISAWA, Assoc. Prof.

Jun ADACHI, Assoc. Prof.

Tadayoshi FUSHIKI, Assist. Prof.

Ying CAO, Assist. Prof.

Computational Mathematics Group
Yoshiaki ITOH, Prof.

 Department of Data Science

Takashi TSUCHIYA, Prof.

Survey Research Group

Satoshi ITO, Assoc. Prof.

Yoshiyuki SAKAMOTO (Director), Prof.

Yoshihiko MIYASATO, Assoc. Prof.

Takashi NAKAMURA, Prof.
Ryozo YOSHINO, Prof.
Nozomu MATSUBARA, Visiting Prof.
Nicolas Emmanuel SYNODINOS, Visiting Prof.
(2006.8.1 - 2006.9.30)(PKDRC)
Molecular Evolution Research Group
Masami HASEGAWA (Director), Prof.

Hajime IHARA, Assoc. Prof.

Tetsuo HASHIMOTO, Visiting Prof.

Tadahiko MAEDA, Assoc. Prof.

Jun ADACHI, Assoc. Prof.

Takahiro TSUCHIYA, Assoc. Prof.

Ying CAO, Assist. Prof.

Wataru MATSUMOTO, Assist. Prof.

Masato NIKAIDO, Research Fellow upon JSPS program

Multidimensional Data Analysis Group
Takemi YANAGIMOTO, Prof.
Yasumasa BABA, Prof.
Toshiharu FUJITA, Prof.
Masahiro MIZUTA, Visiting Prof.
Nobuhisa KASHIWAGI, Assoc. Prof.

32

 Prediction and Knowledge Discovery Research Center

Yuriko NISHIMOTO, Project Researcher
Hideya MITSUI, Graduate Student,
The Graduate University for Advanced Studies
Takahiro YONEZAWA, Graduate Student, University of Tokyo
Yumie MURATA, Graduate Student, University of Tokyo
Data Assimilation Research Group

Satoshi YAMASHITA, Assoc. Prof.

Tomoyuki HIGUCHI (Vice Director-General), Prof.

Sumie UEDA, Assist. Prof.

Masafumi KAMACHI, Visiting Prof.

Takashi WASHIO, Visiting Assoc. Prof.

Naoto KUNITOMO, Visiting Prof.

Genta UENO, Assist. Prof.

Hiroshi TSUDA, Visiting Prof.

Alexandre TERMIER, Project Researcher

Satoshi YAMASHITA, Assoc. Prof.

Shinya NAKANO, Researcher, JST CREST

Seisho SATO, Assoc. Prof.

Statistical Seismology Research Group

Yoshinori KAWASAKI, Assoc. Prof.

Yosihiko OGATA, Prof.

Toshinao YOSHIBA, Visiting Assoc. Prof.

Shinji TOHDA, Visiting Prof.

Yoko TANOKURA, Project Researcher

David Shamus HARTE, Visiting Assoc. Prof. (2006.7.24 - 2006.9.22)

Masayuki KUMON, Project Researcher

Yasuaki MURATA, Assoc. Prof., Institute of Geoscience,

Ken-ichi KAWAI, Project Researcher

Geological Survey of Japan, AIST
Takaki IWATA, Researcher upon Grant-in-aid for
Scientific Research Project Researcher
Marco CUTURI, Project Researcher

Jiancang ZHUANG, Research Associate, UCLATor Andre MYRVOLL, Project Researcher

Kazuyoshi NANJO, Research Associate, ETH

Shohei SHIMIZU, Researcher Fellow upon JSPS program

Masatsugu WAKAURA, Graduate Student,

Teruhisa SUGIMOTO, Project Research

The Graduate University for Advanced Studies

Yoshiyuki TSUDA, Project Reseacher

Ushio TANAKA, Graduate Student,
The Graduate University for Advanced Studies
Statistical Genome Diversity Research Group
Shinto EGUCHI, Prof.
Hirofumi WAKAKI, Visiting Prof.
Mihoko MINAMI, Assoc. Prof.
Shiro IKEDA, Assoc. Prof.
Hironori FUJISAWA, Assoc. Prof.
Tadayoshi FUSHIKI, Assist. Prof.
Masanori KAWAKITA, Project Researcher

Junji NAKANO (Director), Prof.
Satoshi YAMASHITA, Assoc. prof

Organization

Satoshi KURIKI, Prof.

 Center for Engineering and Technical Support

Setsuko TERAO, Chief of Supervision Unit (Concurrent)
Computing Facility Unit
Kohichi KATSURA, Chief of Computing Facility Unit
Networking Facility Unit
Kazuhiro NAKAMURA, Chief of Networking Facility Unit
Education and Library Unit
Setsuko TERAO, Chief of Education and Library Unit
Public Outreach Unit

 Risk Analysis Research Center (RARC)

Yuriko WATANABE, Chief of Public Outreach Unit

Food and Drug Safety Research Group
Takemi YANAGIMOTO, Prof.
Toshiharu FUJITA, Prof.
Hiroe TSUBAKI (Director), Visiting Prof.

 Administration Office
Toshifumi HAGIWARA, Director of Administration Office
General Administration Section

Toshiya SATO, Visiting Prof.

Kuniaki USUI, Head of General Administration Section

Kunihiko HAYASHI, Visiting Prof.

Shigeyuki FUJII, Deputy Head

Satoshi AOKI, Visiting Assoc. Prof.

of General Administration Section

Toshimitsu HAMASAKI, Visiting Assoc. Prof.

Fumio SUTŌ, Chief of General Affairs Unit

Yoshimitsu HIEJIMA, Visiting Assoc. Prof.

Masami SAKAO, Chief of Personnel Unit

Takaaki SHIMURA, Assist. Prof.

Tsutomu ISOYAMA, Chief of Research Cooperation Unit

Environmental Risk Research GroupAccounting Section

Yukio MATSUMOTO, Visiting Prof.

Tatsuya HITOMI, Head of Accounting Section

Kazuo YAMAMOTO, Visiting Prof.

Kazue WATANABE, Deputy Head of Accounting Section

Yoshiro ONO, Visiting Prof.

Minoru HAGIWARA, Chief of General Affairs Unit

Nobuhisa KASHIWAGI, Assoc. Prof.

Minoru HAGIWARA, Chief of Auditing Unit (Concurrent)

Koji KANEFUJI, Assoc. Prof.

Yoshiyuki SAKATA, Chief of Supplies Unit

Hideshige TAKADA, Visiting Assoc. Prof.

Hirotomo SHIMIZU, Chief of Property Maintenance Unit

Hirokazu TAKANASHI, Visiting Assoc. Prof.
Toshihiko KAWAMURA, Assist. Prof.
Mitsuhiro TOMOSADA, Project Researcher
Financial Risk and Insurance Research Group
Ryozo MIURA, Visiting Prof.

33

Council of The Institute of Statistical Mathematics
Yutaka KANO
Toshinari KAMAKURA
Naoto KUNITOMO
Sadanori KONISHI
Yoshiharu SATO
Makoto TAIJI
Katsuto TANAKA
Fumi HAYASHI
Yoshihiko MIZUMOTO

As of April 1, 2006

Professor, Graduate School of Engineering, Osaka University
Professor, Faculty of Science and Engineering, Chuo University
Professor, Graduate School of Economics, the University of Tokyo
Professor, Graduate School of Mathematics, Kyushu University
Professor, Graduate School of Engineering, Hokkaido University
Team Leader, Genomic Sciences Center, RIKEN
Professor, Graduate School of Economics, Hitotsubashi University
Professor, Department of Human Sciences, Toyo Eiwa University
Chief of Astronomical Data Analysis Center, National Astronomical Observatory of Japan,
National Institutes of Natural Sciences

Organization

Satoru MIYANO
Masaharu TANEMURA
Yoshiyasu TAMURA
Tomoyuki HIGUCHI
Makio ISHIGURO
Yoshiyuki SAKAMOTO
Katsuomi HIRANO
Masami HASEGAWA
Junji NAKANO
Yosihiko OGATA
Shinto EGUCHI
Satoshi KURIKI

Professor, Human Genome Center, the Institute of Medical Science of the University of Tokyo
Professor (Vice Director-general, ISM (General Affairs))
Professor (Vice Director-general, ISM (Assessment))
Professor (Vice Director-general, ISM (Research Planning))
Professor (Director of Department of Statistical Modeling, ISM)
Professor (Director of Department of Data Science, ISM)
Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM)
Professor (Director of Prediction and Knowledge Discovery Research Center, ISM)
Professor (Center for Engineering and Technical Support, ISM)
Professor (Department of Statistical Modeling, ISM)
Professor(Department of Mathematical Analysis and Statistical Inference, ISM)
Professor(Department of Mathematical Analysis and Statistical Inference, ISM)

Cooperative Research Committee
Hiroshi KOJIMAAs of April 1, 2006

Chief of the Department of International Research and Cooperation,
National Institute of Population and Social Security Research

Toshinari KAMAKURA
Yoshihiro YAJIMA
Masahiro MIZUTA
Manabu IWASAKI
Takashi NAKAMURA
Junji NAKANOTakashi TSUCHIYA
Yukito IBA

Professor, Department of Science and Engineering, Chuo University
Professor, Graduate School of Economics, the University of Tokyo
Professor, Information Initiative Center, Hokkaido University
Professor, Department of Industrial Engineering and Information Sciences, Seikei University
Professor (Department of Data Science, ISM)
Professor (Chief of Center for Engineering and Technical Support, ISM)
Professor (Department of Mathematical Analysis and Statistical Inference, ISM)
Associate Professor (Department of Statistical Modeling, ISM)

Emeritus Professor
Hirojiro AOYAMA
Kameo MATUSITA
Sigeki NISIHIRA
Tatsuzo SUZUKI

34

As of April 1, 2006

Hirotugu AKAIKE
Giitiro SUZUKI
Ryoichi SHIMIZU
Noboru OHSUMI

Masakatsu MURAKAMI
Kunio TANABE
Tadashi MATSUNAWA

June, 1944

Based on a proposal submitted at an academic study conference in December 1943, theorganization was founded as an institute under the direct control of the Ministry of Education.
This proposal aimed to provide supervision for studies looking into the mathematical principles ofprobability and their application, and was also intended to facilitate, unify and promote thepublication of research results.

April, 1947

The affiliated statistical specialists’ school was opened.

May, 1947

The Institute was divided into the 1st Research Dept. (fundamental theories), the 2nd Research
Dept. (statistical theories for the natural sciences), and the 3rd Research Dept. (statistical theories
for the social sciences).

June, 1949

The Institute was placed under the control of the Ministry of Education because of theenforcement of the Ministry of Education Establishment Law.

September, 1955 Reorganized into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (naturaland social science theories), and the 3rd Research Dept. (operations, research, statistical analysis
theories). The laboratory system, comprising 9 laboratories and the research guidance promotion
room, was adopted.
October, 1969

A new office building was constructed.

April, 1971

The 4th Research Dept. (informatics theories) was instituted.

April, 1973

The 5th Research Dept. (prediction and control theories) was instituted.

October, 1975

The 6th Research Dept. (statistical theories of human behavior) was instituted.

November, 1979

The Information Research Building was constructed.

April, 1985

Reorganized as an Inter-University Research Institute owing to the revision of the Order for theHistory

Enforcement of the National school Establishment Law. The revised law required that the Institutewould, as an National Inter-University Research Institute, 1) conduct studies on statisticalmathematics and its application, 2) provide opportunities for university teachers or otherresearchers majoring in this field to utilize the facility, and 3) contribute to the development of
academic studies in universities.
At the same time, the 6 research departments were reorganized into 4 research departments(Fundamental Statistical Theory, Statistical Methodology, Prediction and Control, andInterdisciplinary Statistics). The Statistical Data Analysis Center and the Statistical Education andInformation Center were instituted, and the affiliated Statistical Technician Training Center was
abolished.
October, 1988

The Dept. of Statistical Science was instituted in the School of Mathematical and PhysicalScience, part of the Graduate University for Advanced Studies (SOKENDAI).

June, 1989

The Institute was reorganized as an Inter-University Research Institute based on the National
School Establishment Law.

April, 1993

The Planning Coordination Chief System was instituted.

April, 1997

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into
the Center for Information on Statistical Sciences.

September, 2003 The Prediction and Knowledge Discovery Research Center was instituted in the affiliated facility.
April, 2004

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research
Organization of Information and Systems of the Inter-University Research Institute based on the
National University Corporation Law. The Planning Coordination Chief System was abolished andthe position of Vice Director-General was instituted instead. The Dept. of Statistical Science in the
School of Mathematical and Physical Science, SOKENDAI, was reorganized. In addition, theDept. of Statistical Science and the School of Multidisciplinary Sciences were instituted.

35

April, 2005

The research organization was reorganized into three research departments (the Department of
Statistical Modeling, the Department of Data Science, and the Department of Mathematical
Analysis and Statistical Inference). The affiliated Center for Development of Statistical Computing,
the Center for Information on Statistical Sciences, and the Engineering and Technical Services
Section were integrated into the Center for Engineering and Technical Support.
The affiliated facilities were reorganized as research departments, and the Risk Analysis
ResearchCenter was instituted.

1947

1955

History1955

Now36